





'''simple docstring'''






import argparse

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCAmelCase									=   16
lowerCAmelCase									=   32






def   __A    (				a_    :	Accelerator   ,a_    :	int = 1_6						):
		lowerCAmelCase  :     Any       						= AutoTokenizer.from_pretrained("bert-base-cased"						)
		lowerCAmelCase  :     Optional[int]       						= load_dataset("glue"   ,"mrpc"						)

		def tokenize_function(a_    :	Optional[Any]						):
				# max_length=None => use the model max length (it's actually the default)
				lowerCAmelCase  :     Any       						= tokenizer(examples["sentence1"]   ,examples["sentence2"]   ,truncation=a_   ,max_length=a_						)
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		# starting with the main process first:
		with accelerator.main_process_first():
				lowerCAmelCase  :     List[str]       						= datasets.map(
				    a_   ,batched=a_   ,remove_columns=["idx", "sentence1", "sentence2"]   ,)

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		lowerCAmelCase  :     int       						= tokenized_datasets.rename_column("label"   ,"labels"						)

		def collate_fn(a_    :	Union[str, Any]						):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				lowerCAmelCase  :     Optional[Any]       						= 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						lowerCAmelCase  :     str       						= 1_6
				elif accelerator.mixed_precision != "no":
						lowerCAmelCase  :     Any       						= 8
				else:
						lowerCAmelCase  :     List[str]       						= None

				return tokenizer.pad(
				    a_   ,padding="longest"   ,max_length=a_   ,pad_to_multiple_of=a_   ,return_tensors="pt"   ,)

		# Instantiate dataloaders.
		lowerCAmelCase  :     int       						= DataLoader(
		    tokenized_datasets["train"]   ,shuffle=a_   ,collate_fn=a_   ,batch_size=a_   ,drop_last=a_						)
		lowerCAmelCase  :     List[Any]       						= DataLoader(
		    tokenized_datasets["validation"]   ,shuffle=a_   ,collate_fn=a_   ,batch_size=a_   ,drop_last=(accelerator.mixed_precision == "fp8")   ,)

		return train_dataloader, eval_dataloader






def   __A    (				a_    :	str   ,a_    :	Optional[int]						):
		# Initialize accelerator
		lowerCAmelCase  :     Optional[Any]       						= Accelerator(cpu=args.cpu   ,mixed_precision=args.mixed_precision						)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowerCAmelCase  :     Optional[int]       						= config["lr"]
		lowerCAmelCase  :     Optional[Any]       						= int(config["num_epochs"]						)
		lowerCAmelCase  :     List[str]       						= int(config["seed"]						)
		lowerCAmelCase  :     Union[str, Any]       						= int(config["batch_size"]						)

		lowerCAmelCase  :     List[str]       						= evaluate.load("glue"   ,"mrpc"						)

		# If the batch size is too big we use gradient accumulation
		lowerCAmelCase  :     str       						= 1
		if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
				lowerCAmelCase  :     Dict       						= batch_size // MAX_GPU_BATCH_SIZE
				lowerCAmelCase  :     Optional[int]       						= MAX_GPU_BATCH_SIZE

		set_seed(a_						)
		lowerCAmelCase						,		lowerCAmelCase  :     Tuple       						= get_dataloaders(a_   ,a_						)
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowerCAmelCase  :     Any       						= AutoModelForSequenceClassification.from_pretrained("bert-base-cased"   ,return_dict=a_						)

		# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
		# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
		# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
		lowerCAmelCase  :     List[Any]       						= model.to(accelerator.device						)
		# Instantiate optimizer
		lowerCAmelCase  :     Dict       						= AdamW(params=model.parameters()   ,lr=a_						)

		# Instantiate scheduler
		lowerCAmelCase  :     int       						= get_linear_schedule_with_warmup(
		    optimizer=a_   ,num_warmup_steps=1_0_0   ,num_training_steps=(len(a_						) * num_epochs) // gradient_accumulation_steps   ,)

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.

		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Dict       						= accelerator.prepare(
		    a_   ,a_   ,a_   ,a_   ,a_						)

		# Now we train the model
		for epoch in range(a_						):
				model.train()
				for step, batch in enumerate(a_						):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device						)
						lowerCAmelCase  :     Union[str, Any]       						= model(**a_						)
						lowerCAmelCase  :     List[str]       						= outputs.loss
						lowerCAmelCase  :     int       						= loss / gradient_accumulation_steps
						accelerator.backward(a_						)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				for step, batch in enumerate(a_						):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device						)
						with torch.no_grad():
								lowerCAmelCase  :     List[str]       						= model(**a_						)
						lowerCAmelCase  :     List[Any]       						= outputs.logits.argmax(dim=-1						)
						lowerCAmelCase						,		lowerCAmelCase  :     Optional[int]       						= accelerator.gather_for_metrics((predictions, batch["labels"])						)
						metric.add_batch(
						    predictions=a_   ,references=a_   ,)

				lowerCAmelCase  :     int       						= metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'''epoch {epoch}:'''   ,a_						)






def   __A    (				):
		lowerCAmelCase  :     Optional[Any]       						= argparse.ArgumentParser(description="Simple example of training script."						)
		parser.add_argument(
		    "--mixed_precision"   ,type=a_   ,default=a_   ,choices=["no", "fp16", "bf16", "fp8"]   ,help="Whether to use mixed precision. Choose"
		    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
		    "and an Nvidia Ampere GPU."   ,)
		parser.add_argument("--cpu"   ,action="store_true"   ,help="If passed, will train on the CPU."						)
		lowerCAmelCase  :     Optional[Any]       						= parser.parse_args()
		lowerCAmelCase  :     Dict       						= {"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
		training_function(a_   ,a_						)


if __name__ == "__main__":
					main()










'''simple docstring'''






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for data in source_data:
				for i, el in enumerate(a_						):
						if len(a_						) < i + 1:
								data_lists.append([]						)
						data_lists[i].append(float(a_						)						)
		return data_lists






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for dlist, weight in zip(a_   ,a_						):
				lowerCAmelCase  :     Optional[Any]       						= min(a_						)
				lowerCAmelCase  :     List[str]       						= max(a_						)

				lowerCAmelCase  :     list[float]       						= []
				# for weight 0 score is 1 - actual score
				if weight == 0:
						for item in dlist:
								try:
										score.append(1 - ((item - mind) / (maxd - mind))						)
								except ZeroDivisionError:
										score.append(1						)

				elif weight == 1:
						for item in dlist:
								try:
										score.append((item - mind) / (maxd - mind)						)
								except ZeroDivisionError:
										score.append(0						)

        # weight not 0 or 1
				else:
						lowerCAmelCase  :     List[Any]       						= f'''Invalid weight of {weight:f} provided'''
						raise ValueError(a_						)

				score_lists.append(a_						)

		return score_lists






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[float]       						= [0 for i in range(len(score_lists[0]						)						)]

		for slist in score_lists:
				for j, ele in enumerate(a_						):
						lowerCAmelCase  :     Optional[Any]       						= final_scores[j] + ele

		return final_scores






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     Union[str, Any]       						= get_data(a_						)
		lowerCAmelCase  :     List[Any]       						= calculate_each_score(a_   ,a_						)
		lowerCAmelCase  :     Optional[Any]       						= generate_final_scores(a_						)

		# append scores to source data
		for i, ele in enumerate(a_						):
				source_data[i].append(a_						)

		return source_data



